Abstract
Entomological radar is an effective means of monitoring insect migration, and can realize long-distance and large-scale rapid monitoring. The stable tracking of individual insect targets is the basic premise underlying the identification of insect species and the study of insect migration mechanisms. However, the complex motion trajectory and large number of false measurements decrease the performance of insect target tracking. In this paper, an insect target tracking algorithm in clutter was designed based on the multidimensional feature fusion strategy (ITT-MFF). Firstly, multiple feature parameters of measurements were fused to calculate the membership of measurements and target, thereby improving the data association accuracy in the presence of clutter. Secondly, a distance-correction factor was introduced to the probabilistic data association (PDA) algorithm to accomplish multi-target data association with a low computational cost. Finally, simulation scenarios with different target numbers and clutter densities were constructed to verify the effectiveness of the proposed method. The tracking result comparisons of the experimental data acquired from a Ku-band entomological radar also indicate that the proposed method can effectively reduce computational cost while maintaining high tracking precision, and is suitable for engineering implementation.
Highlights
The joint probabilistic data association algorithm (JPDA) algorithm can maintain an excellent performance in the case of dense clutter, but the expensive computational cost is a critical problem in applying the algorithm
The method proposed in this paper fully considers the scattering characteristics of insect targets and improves the data association accuracy in cluttered environments based on multidimensional feature fusion strategy
An insect target tracking algorithm in clutter based on the multidimensional feature fusion strategy was proposed
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. It is difficult to achieve a balance between computational cost and tracking performance in the existing optimized JPDA algorithm, which makes it difficult to ensure excellent performance in insect target tracking scenarios Another challenging problem for multitarget tracking is differentiating between measurements arising from the target of interest and measurements originating from other target returns or clutter. In the above algorithm, only a single-dimensional feature is utilized, making it difficult to achieve excellent performance in complex insect target tracking scenarios. In this paper, an insect target tracking algorithm in clutter was designed based on the multidimensional feature fusion strategy, which is mainly aimed at data association processing, one of the most important aspects of tracking.
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